• There are various data types and sources available to marketing leaders for post-sale customer engagement purposes
  • Choosing and leveraging the right data depends on which phases of the customer lifecycle your organization is addressing
  • At SiriusDecisions Summit this year, John Donlon and Kristin Farwell will discuss a method to identify data necessary for post-sale engagement

The importance of thoughtful, personalized post-sale customer engagement is not a new concept – but making it a reality has its challenges. As buyers become customers, and their needs change from purchase to usage or execution, organizations need to determine a strategy to execute meaningful interactions across the customer lifecycle. High quality, connected data is a foundational element that informs the strategy and crafts this ideal experience. In fact, organizations could potentially do more harm than good if inappropriate messages or misguided tactics are leveraged due to inaccurate, inconsistent or incomplete data.

Identifying Post-Sale Needs and Aligning Them to Interactions

The process begins by identifying post-sale needs of the customer and organization, and the interactions necessary to support those needs across the various stages of the customer lifecycle:

  • Identify buyer roles. In the same manner that buyers have different roles in the purchase process, customers have different roles in the ongoing customer lifecycle. The first step is to identify these customer roles and their unique attributes (e.g. needs, challenges, motivations). For example, a customer who is a software end user likely requires very different touchpoints than an executive or non-user.
  • Determine post-sale interactions. Once customer roles have been determined, the next step is to identify the appropriate post-sale engagements that address both the need of a customer, as well as the goals of the organization. For example, an effective onboarding program enables organizations to equip customers to become productive as quickly and easily as possible, which also serves the customer’s need to quickly realize value from their purchase.

Identifying Data Requirements

Because of the various interaction types, it’s important to understand what data is required for each. For example, an interaction might be initiated by the organization (e.g. a kickoff call), in which case it’s necessary to have the individual’s name and relevant contact information. Or it might be self-guided (e.g. taking an online training course), where account-specific data may be leveraged to allow them access.

When defining the types of insights required, it can be helpful to put them into the categories of profile, activity and derived data. Profile data tells us about who the customer is – demographic and firmographic details. Activity data covers both their interactions with your organization – e.g. usage of a trial version of your product – as well as activities not involving your organization (e.g. the customer hiring a new CFO). Finally, derived data is inferred from other data (e.g. predictions for the next best offer, results from a customer satisfaction survey).

Join us at SiriusDecisions Summit in May as we explore strategies to help organizations understand how customer engagement processes are affected by data, and learn strategies for ensuring those activities are fueled with the most accurate and complete information possible.

See this session and many more at SiriusDecisions Summit 2018! Check out the full agenda here and register here.